Gapminder, Life Expectancy and Income

The following packages are used in this project:

library(tidyverse) # For data wrangling
library(readxl) # To import excel and csv files
library(countrycode) # Assigns the names of the continents for each country
library(readr) # Exports the dataframe
library(ggthemes) # Additional themes for graphics
library(plotly) # Creates interactive plots
library(gganimate) # Creates animated plots
library(gifski) # Creates a GIF animation

Introduction

In this project, my goal is to recreate and visualize three single-year plots representing the years 1800, 1950, and 2018, as demonstrated in Hans Rosling’s captivating video on global development. The video, titled “Gapminder, Hans Rosling’s 200 Countries, 200 Years, 4 Minutes - The Joy of Stats,” showcases the relationship between life expectancy and wealth across nations.

By recreating these plots, I aim to gain insights into how life expectancy and wealth have evolved over time. The selected years of 1800, 1950, and 2018 provide us with a historical perspective and reflect the most recent available data for comprehensive analysis.

• Gapminder, Hans Rosling’s 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four

life expectancy and wealth, 2009

Furthermore, I will utilize the gganimate package to create an animated plot encompassing the past 50 years of data. This animated visualization will allow us to observe the changes in life expectancy and wealth trends over this period, emphasizing any notable patterns or shifts.

Lastly, I will leverage the plotly package to construct an interactive plot with the latest data from the year 2018. This interactive visualization will enable users to explore the data by interacting with various elements such as tooltips, zooming, and selecting specific countries of interest.

Through this project, I aim to present the data in a visually compelling and engaging manner, providing a deeper understanding of the intricate relationship between life expectancy, wealth, and the progression of global development.

You can find the GitHub repository at https://github.com/DS-Jose/gapminder

Data Files

The project utilizes the following data files, which have been downloaded from Gapminder and can be found in the “data” folder of this project:

• Income: “data/income_per_person_gdppercapita_ppp_inflation_adjusted.csv”
  • This file contains information about the income per person, adjusted for inflation and purchasing power parity (PPP).
• Life Expectancy (years): “data/life_expectancy_years.csv”
  • This file provides data on life expectancy in years, capturing the average lifespan of individuals across different countries.
• Population: “data/population_total.csv”
  • The population file includes data on the total population count for each country.

Make sure to load and process these files in your code to obtain the necessary variables for creating the desired plots.

Importing Data

Import the three CSV datasets for processing: income or GDP per capita, life expectancy, and population.

# Importing the datasets and assigning shorter names

# Use `check.names = FALSE` to prevent variable name changes after importing.

inc <- read.csv("data/income_per_person_gdppercapita_ppp_inflation_adjusted.csv", check.names = FALSE)

lif <- read.csv("data/life_expectancy_years.csv", check.names = FALSE)

pop <- read.csv("data/population_total.csv", check.names = FALSE)

In these datasets, the continent names for each country are missing. To rectify this, we can obtain the continent names by importing an additional file that maps countries to continents or by utilizing the countrycode package.

# Adding the continent to the life expectancy table using countrycode package
# This step is performed to associate each country in the life expectancy table with its respective continent for further analysis.

lif$continent <- countrycode(sourcevar = lif[, "country"],
                            origin = "country.name",
                            destination = "continent")

Tidy Data

The original datasets we have imported do not adhere to the principles of tidy data. Tidy data is a structured format that facilitates easier analysis and visualization by organizing data into consistent patterns. In the current state, our datasets have multiple columns representing different years, which violates the tidy data principles.

To rectify this, we need to pivot longer all three tables to transform them into tidy format. Pivoting longer involves reshaping the data so that each year becomes a separate observation and the associated values are placed in a new column. By pivoting the data, we can effectively organize and utilize it for further analysis.

# Pivot the datasets to transform them into tidy format

life_expectancy_long <- lif %>%
  # Pivot longer the columns, except for country and continent
  pivot_longer(cols = c(-country, -continent),
               # Create new variables: year and life_expectancy
               names_to = "year",
               values_to = "life_expectancy")

income_long <- inc %>% 
  pivot_longer(-country,
               names_to = "year",
               values_to = "income")

population_long <- pop %>% 
  pivot_longer(-country,
               names_to = "year",
               values_to = "population")

Joins

To ensure accuracy and consistency in our analysis, we need to consider the presence of projected data in the income and population datasets. These projections may introduce uncertainties and potentially skew our findings. To address this concern, we will rely on the life expectancy table, which provides the latest available data without any projections.

By using the life expectancy table as our primary dataset, we can maintain the integrity of our analysis and avoid potential biases introduced by projected values. We will perform left joins to combine the income and population datasets with the life expectancy table, aligning the data based on country and year.

# Using a left join to merge the income and population datasets with the life expectancy table

# Joining the income_long dataset with the life_expectancy_long dataset based on country and year
allcountries <- life_expectancy_long %>%
  left_join(income_long, by = c("country", "year"))

# Joining the population_long dataset with the existing allcountries dataset based on country and year
allcountries <- allcountries %>%
  left_join(population_long, by = c("country", "year"))

# Checking the first few rows of the joined data
head(allcountries)

## # A tibble: 6 × 6
##   country     continent year  life_expectancy income population
##   <chr>       <chr>     <chr>           <dbl>  <int>      <int>
## 1 Afghanistan Asia      1800             28.2    603    3280000
## 2 Afghanistan Asia      1801             28.2    603    3280000
## 3 Afghanistan Asia      1802             28.2    603    3280000
## 4 Afghanistan Asia      1803             28.2    603    3280000
## 5 Afghanistan Asia      1804             28.2    603    3280000
## 6 Afghanistan Asia      1805             28.2    603    3280000

Exporting Data

By exporting the clean dataset as a CSV file, named “clean_df.csv,” you can conveniently store and utilize the cleaned data in subsequent analyses or share it with others. This step ensures that the progress made in importing and cleaning the data is preserved and easily accessible in the future.

# Exporting the clean dataset as a CSV file for future use and manipulation
write_csv(allcountries, "clean_df.csv")

Plotting

Using ggplot

For the static plots I decided to go with the years 1800, 1950 and 2018 to compare how the variables income and life expectancy changed over this years.

# Assigning a name to the plot
chart1 <- allcountries %>% 
    # Filtering the data for the year 1800
    filter(year %in% c("1800")) %>%
    ggplot(aes(x = income, y = life_expectancy, colour = continent)) +
    # Adding scatter plot points with population as size and transparency
    geom_point(aes(size = population), alpha = .5) + 
    # Setting limits and breaks for the y-axis
    scale_y_continuous(limits = c(10, 95), breaks = c(25, 50, 75)) +
    # Setting logarithmic scale for the x-axis
    scale_x_log10(limits = c(400, 120000), breaks = c(400, 4000, 40000))
# Displaying the plot for the year 1800
chart1

# Repeating the process for the years 1950 and 2018
chart2 <- allcountries %>% 
    filter(year %in% c("1950")) %>%
    ggplot(aes(x = income, y = life_expectancy, colour = continent)) +
    geom_point(aes(size = population), alpha = .5) + 
    scale_y_continuous(limits = c(10, 95), breaks = c(25, 50, 75)) +
    scale_x_log10(limits = c(400, 120000), breaks = c(400, 4000, 40000))
# Displaying the plot for the year 1950
chart2

chart3 <- allcountries %>% 
    filter(year %in% c("2018")) %>%
    ggplot(aes(x = income, y = life_expectancy, colour = continent)) +
    geom_point(aes(size = population), alpha = .5) + 
    scale_y_continuous(limits = c(10, 95), breaks = c(25, 50, 75)) +
    scale_x_log10(limits = c(400, 120000), breaks = c(400, 4000, 40000))
# Displaying the plot for the year 2018
chart3

Finding Relevant Values

Finding interesting data points to show in our plots, including maximum and minimum values for income and life expectancy.

# For the year 1800
# Extracting the country with the highest life expectancy in 1800
allcountries %>% 
  filter(year %in% c("1800")) %>%
  slice_max(life_expectancy)

## # A tibble: 1 × 6
##   country continent year  life_expectancy income population
##   <chr>   <chr>     <chr>           <dbl>  <int>      <int>
## 1 Iceland Europe    1800             42.9    926      61400

# Extracting the country with the highest income in 1800
allcountries %>% 
  filter(year %in% c("1800")) %>%
  slice_max(income)

## # A tibble: 1 × 6
##   country     continent year  life_expectancy income population
##   <chr>       <chr>     <chr>           <dbl>  <int>      <int>
## 1 Netherlands Europe    1800             39.9   4230    2250000

# For the year 1950
# Extracting the country with the highest life expectancy in 1950
allcountries %>% 
  filter(year %in% c("1950")) %>%
  slice_max(life_expectancy)

## # A tibble: 1 × 6
##   country continent year  life_expectancy income population
##   <chr>   <chr>     <chr>           <dbl>  <int>      <int>
## 1 Norway  Europe    1950             71.6  11400    3270000

# Extracting the country with the highest income in 1950
allcountries %>% 
  filter(year %in% c("1950")) %>%
  slice_max(income)

## # A tibble: 1 × 6
##   country continent year  life_expectancy income population
##   <chr>   <chr>     <chr>           <dbl>  <int>      <int>
## 1 Brunei  Asia      1950             55.5  56600      48000

# Extracting the country with the lowest life expectancy in 1950
allcountries %>% 
  filter(year %in% c("1950")) %>%
  slice_min(life_expectancy)

## # A tibble: 1 × 6
##   country continent year  life_expectancy income population
##   <chr>   <chr>     <chr>           <dbl>  <int>      <int>
## 1 Yemen   Asia      1950             23.8   1340    4400000

# For the year 2018
# Extracting the country with the highest life expectancy in 2018
allcountries %>% 
  filter(year %in% c("2018")) %>%
  slice_max(life_expectancy)

## # A tibble: 1 × 6
##   country continent year  life_expectancy income population
##   <chr>   <chr>     <chr>           <dbl>  <int>      <int>
## 1 Japan   Asia      2018             84.2  39100  127000000

# Extracting the country with the lowest life expectancy in 2018
allcountries %>% 
  filter(year %in% c("2018")) %>%
  slice_min(life_expectancy)

## # A tibble: 1 × 6
##   country continent year  life_expectancy income population
##   <chr>   <chr>     <chr>           <dbl>  <int>      <int>
## 1 Lesotho Africa    2018             51.1   2960    2260000

Plots Formatting

To make the plots more attractive, we need to use the right formatting. In the code below I am adding themes, changing colors, adding labels and reference lines.

# Saving the final plots
lifeexp1800 <- chart1 +
# Adding labels to show information about the plot, like title, labels.
  labs(title = "Income versus Life Expectancy in 1800",
       x = "Income (GDP per capita in USD $)",
       y = "Life expectancy (years)",
       caption = "Source: Gapminder",
       size = "Population (millions)",
       color = "Continent") +
# Setting the size of the bubbles with the same scale for the 3 plots
  scale_size(
      # The range is the size of the bubbles, higher would mean bigger difference between bubbles.
       range = c(0.1, 15),
      # The limits of population from NA to the highest which is China just under 1500 million.
       limits = c(NA,1500000000), 
      # Multiplying the population by millions so it would be easier to read.
       breaks = 1000000 * c(10, 50, 100, 500, 1000, 1500),
       labels = c("10", "50", "100", "500", "1000", "1500")) +
# Changing the color palette
  scale_colour_brewer(palette = "Set1") +
# Changing the default theme
  theme_classic() +
# Using the override function to make the color label bigger
  guides(color = guide_legend(override.aes = list(size = 5, alpha = .5))) +
# Adding the vertical and horizontal lines to replicate Hans Rosling's plots
  geom_vline(xintercept = c(400, 4000, 40000),
             # Adding a light color, with small size and .5 transparency so it is not distracting.
                color = "grey", size = .2, alpha = .5) +
# Doing the same on the y axes.  
  geom_hline(yintercept = c(25, 50, 75), 
                color = "grey", size = .2, alpha = .5) +
# Labeling the countries with the interesting points we found earlier.
# The life_expectancy has a +5 to show the label higher on the y axes.
  geom_text(aes(x = income, y = life_expectancy + 5, label = country),
               color = "grey50",
            # Filtering the data to show only the 2 countries we want.
               data = filter(allcountries, year == 1800, country %in% c("Iceland", "Netherlands")))

# Showing the result
lifeexp1800

# Repeating the same process for the next 2 plots.
lifeexp1950 <- chart2 + 
  labs(title = "Income versus Life Expectancy in 1950",
       x = "Income (GDP per capita in USD $)",
       y = "Life expectancy (years)",
       caption = "Source: Gapminder",
       size = "Population (millions)",
       color = "Continent") +
  scale_size(
       range = c(0.1, 15),    
       limits = c(NA,1500000000), 
       breaks = 1000000 * c(10, 50, 100, 500, 1000, 1500),
       labels = c("10", "50", "100", "500", "1000", "1500")) +
  scale_colour_brewer(palette = "Set1") +  
  theme_classic() +
  guides(color = guide_legend(override.aes = list(size = 5))) +
  geom_vline(xintercept = c(400, 4000, 40000), 
                color = "grey", size = .2, alpha = .5) +
  geom_hline(yintercept = c(25, 50, 75), 
                color = "grey", size = .2, alpha = .5) +
  geom_text(aes(x = income, y = life_expectancy + 5, label = country),
            color = "grey50",
            data = filter(allcountries, year == 1950, country %in% c("Norway", "Brunei", "Yemen")))

lifeexp1950

lifeexp2018 <- chart3 + 
  labs(title = "Income versus Life Expectancy in 2018",
       x = "Income (GDP per capita in USD $)",
       y = "Life expectancy (years)",
       caption = "Source: Gapminder",
       size = "Population (millions)",
       color = "Continent") +
  scale_size(
       range = c(0.1, 15),    
       limits = c(NA,1500000000), 
       breaks = 1000000 * c(10, 50, 100, 500, 1000, 1500),
       labels = c("10", "50", "100", "500", "1000", "1500")) +
  scale_colour_brewer(palette = "Set1") +
  theme_classic() +
  guides(color = guide_legend(override.aes = list(size = 5))) +
  geom_vline(xintercept = c(400, 4000, 40000), 
                color = "grey", size = .2, alpha = .5) +
  geom_hline(yintercept = c(25, 50, 75), 
                color = "grey", size = .2, alpha = .5) +
  geom_text(aes(x = income, y = life_expectancy + 5, label = country),
                color = "grey50",
                data = filter(allcountries, year == 2018, country %in% c("Japan", "Lesotho")))

lifeexp2018

Exporting Plots as Image Files

To share the plots, you can save them as image files using the ggsave() function from the ggplot2 package. The code snippet below demonstrates how to save the plots as JPEG image files:

# Save the plot 'lifeexp1800' as a JPEG image file named "lifeexp1800.jpg"
ggsave("lifeexp1800.jpg", lifeexp1800, width = 16, height = 9)

# Save the plot 'lifeexp1950' as a JPEG image file named "lifeexp1950.jpg"
ggsave("lifeexp1950.jpg", lifeexp1950, width = 16, height = 9)

# Save the plot 'lifeexp2018' as a JPEG image file named "lifeexp2018.jpg"
ggsave("lifeexp2018.jpg", lifeexp2018, width = 16, height = 9)

In the code snippet above, you need to specify the file name and the desired image extension (e.g., “.jpg”). The plot object to be saved is passed as the second argument (lifeexp1800, lifeexp1950, and lifeexp2018). You can also adjust the width and height parameters to specify the aspect ratio of the saved image.

Creating an Animated Data Visualization

To recreate an animation similar to Hans Rosling’s video video, we can visualize the changes in life expectancy and income over the last 50 years.

# Creating a separate dataset for the animation
anim_data <- allcountries %>%
  # Converting the 'year' variable to integer type
  mutate(year = as.integer(year)) %>%
  # Filtering the data to include only years from 1968 to 2018
  filter(year %in% (1968:2018))

# Creating the animated plot
anim_output <- ggplot(anim_data, aes(income, life_expectancy, size = population, color = continent, frame = year)) +
  # Setting the labels and captions for the plot
  labs(x = "Income (GDP per capita in USD $)",
       y = "Life Expectancy (years)",
       caption = "Source: Gapminder",
       size = "Population (millions)",
       color = "Continent") +
  # Setting the y-axis limits and breaks
  scale_y_continuous(limits = c(10, 95), breaks = c(25, 50, 75)) +
  # Setting the x-axis limits and breaks on a logarithmic scale
  scale_x_log10(limits = c(400, 120000), breaks = c(400, 4000, 40000)) +
  # Setting the color palette
  scale_colour_brewer(palette = "Set1") +
  # Applying the classic theme
  theme_classic() +
  # Adjusting the legend size
  guides(color = guide_legend(override.aes = list(size = 5))) +
  # Adding vertical lines
  geom_vline(xintercept = c(400, 4000, 40000), color = "grey", size = 0.2, alpha = 0.5) +
  # Adding horizontal lines
  geom_hline(yintercept = c(25, 50, 75), color = "grey", size = 0.2, alpha = 0.5) +
  # Adding points to represent the data
  geom_point(aes(), alpha = 0.5) +
  # Setting the size of the points
  scale_size(range = c(0.1, 15),
             limits = c(NA, 1500000000),
             breaks = 1000000 * c(10, 50, 100, 500, 1000, 1500),
             labels = c("10", "50", "100", "500", "1000", "1500")) +
  # Adding the year as a title, which changes dynamically with the frames
  ggtitle("Income versus Life Expectancy, year: {frame_time}") +
  # Creating a smooth transition between frames
  transition_time(year) +
  # Setting the transition easing
  ease_aes("linear") +
  # Adding a fade effect for entering frames
  enter_fade() +
  # Adding a fade effect for exiting frames
  exit_fade()

# Creating the animation
animate(anim_output, duration = 10, fps = 20, width = 800, height = 400, renderer = gifski_renderer())

# Saving the animation as a GIF file
anim_save("capstone_animation.gif")

Interactive Data Visualization

To showcase the advanced plotting capabilities in R, I have created an interactive visualization using Plotly. This interactive plot allows you to explore the data in a more engaging way. When you hover your mouse over each dot, you will see detailed information about each country, including population, life expectancy, and income. Additionally, you can zoom in and out, select specific data points, and even export the plot.

# Filtering the data to use the latest year
int_data <- allcountries %>% 
  filter(year=="2018")

# Interactive version, using mutate to create new variables to show in the tooltip
interactive <- int_data %>%
  # Mutating and rounding the income to 0 decimals
  mutate(income = round(income, 0)) %>%
  # Mutating and dividing the population by 1 million for easier reading
  mutate(population = round(population / 1000000, 2)) %>%
  # Mutating life expectancy and rounding to 1 decimal
  mutate(life_expectancy = round(life_expectancy, 1)) %>%
  # Reordering the countries
  arrange(desc(population)) %>%
  mutate(country = factor(country, country)) %>%
  # Text for tooltip
  mutate(text = paste("Country: ", country, "\nPopulation (M): ", population, "\nLife Expectancy: ", life_expectancy, "\nIncome: ", income, sep = "")) %>%
  # Creating the plot
  ggplot(aes(x = income, y = life_expectancy, size = population, color = continent, text = text)) +
  geom_point(aes(size = population), alpha = .5) +
  scale_y_continuous(
    limits = c(50, 90),
    breaks = c(50, 60, 70, 80, 90)
  ) +
  scale_x_log10(
    limits = c(400, 120000),
    breaks = c(400, 4000, 40000)
  ) +
  scale_colour_brewer(palette = "Set1") +
  theme_classic() +
  theme(legend.position = "none") +
  labs(
    title = "Income versus Life Expectancy in 2018",
    x = "Income (GDP per capita in USD $)",
    y = "Life Expectancy (years)",
    caption = "Source: Gapminder"
  )

# Using plotly to make the plot interactive and show each country's information on mouseover
int2018 <- ggplotly(interactive, tooltip = "text")

int2018

This code generates an interactive plot using the latest available data for the year 2018. The plot displays the relationship between income and life expectancy for different countries. Each dot represents a country, and its size corresponds to the country’s population. The color of the dot represents the continent to which the country belongs.

Correlation

Regression model

To explore the relationship between income and life expectancy, we can use a regression model. In R, we can fit a linear regression model using the lm() method. Let’s see how it works:

# Fitting a regression model using lm()
gapminder_model <- lm(income ~ life_expectancy, data = allcountries)

gapminder_model

## 
## Call:
## lm(formula = income ~ life_expectancy, data = allcountries)
## 
## Coefficients:
##     (Intercept)  life_expectancy  
##        -10895.1            359.7

By running the above code, we create a linear regression model where income is the response variable and life expectancy is the predictor variable. The lm() function fits the model to our data from the “allcountries” dataset. The result is stored in the “gapminder_model” object.

To understand the statistical properties of our model, we can obtain a summary:

# Getting a summary of the regression model
summary(gapminder_model)

## 
## Call:
## lm(formula = income ~ life_expectancy, data = allcountries)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -13981  -2717     11   1335 163428 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -10895.085    117.463  -92.75   <2e-16 ***
## life_expectancy    359.708      2.547  141.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8356 on 40435 degrees of freedom
##   (516 observations deleted due to missingness)
## Multiple R-squared:  0.3303, Adjusted R-squared:  0.3303 
## F-statistic: 1.994e+04 on 1 and 40435 DF,  p-value: < 2.2e-16

The summary provides important information about the statistical properties of our model. It includes coefficients, standard errors, t-values, and p-values.

Upon examining the summary, we can conclude that the model is statistically significant because it has a small p-value. However, the R-squared value of 0.33 indicates a weak correlation between income and life expectancy. This means that income alone explains only a small portion of the variation in life expectancy. It’s important to note that there are likely other factors influencing life expectancy that are not included in our model.

Based on this analysis, we cannot rely solely on this model to accurately predict life expectancy in the future. Nonetheless, it provides us with some insights into the relationship between income and life expectancy in the given dataset.

The End

Thank you for reading until the very end, I hope you find this project useful. Special thanks to Martin Monkman for his great teachings in BIDA 302 Programming Fundamentals from the University of Victoria, BC.

If you want to check my other projects please visit my portfolio website at https://ds-jose.github.io/

Resources

• https://bookdown.org/martin_monkman/DataAnalyticsCodingFundamentals/gapminder200.html

• https://towardsdatascience.com/how-to-build-animated-charts-like-hans-rosling-doing-it-all-in-r-570efc6ba382

• https://gganimate.com/index.html

• https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/

• https://www.r-graph-gallery.com/bubble-chart.html